Reimagining research papers as interactive and reliable AI agents
What this work does
Converts a paper and its code into an executable MCP server using environment setup, tutorial execution, reusable tool extraction, iterative testing and server assembly; the scientific LLM consumes the resulting tools, resources and prompts.
Connection to discovery
A concrete example of improving the discovery harness and reusable scientific tools without updating base-model weights. It motivates controlled model-by-harness ablations and checks of execution fidelity versus scientific validity.
Evidence
Inspected same-base-model repository baselines, tutorial versus novel-query evaluation, repository-drift tests, the six-step pipeline and the distinction between causal-gene prioritization and cross-screen perturbation-signature support.
Scope and limitations
Existing perturbation datasets and model predictions are reused; this is not a demonstration of the agent autonomously commissioning a new physical assay. Reference-answer agreement and passing tutorial tests do not establish unique causal identification. The discussion retains human responsibility for scientific direction and interpretation.
THE RESEARCH CONVERSATION
Discussion & reactions
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